By Barry A Bunin; et al
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Another structure-based approach, which is applicable to single or multiple targets, first aims at finding database compounds that are overall consistent with the geometry and chemical features of binding site(s) (for example, by computational docking) and then uses these compounds as templates for focusing, as discussed above. 9 QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS The major goal of QSAR is the evaluation of molecular features that determine biological activity and the prediction of compound potency as a function of structural modifications.
Moreover, dependent on how compound lists were arranged, each chemist rejected different sets of compounds from alternative lists. These findings clearly illustrated the limitations of subjective compound evaluation and selection, regardless of chemical knowledge and experience, and made a strong case for the application of more unbiased computational measures for compound selection. Accordingly, Lajiness and his colleagues developed a multi-component scoring system that made it possible to efficiently screen large source databases and select preferred compounds in a more unbiased and reproducible manner (Lajiness and Shanmugasundaram 2004).
In this example, descriptors and parameters for cell-based partitioning are optimized using principal component analysis as a dimension reduction technique. The chromosome was designed to consist of three bit segments, the first encoding 100 potential descriptors (one per bit position), the second encoding between one and 15 principal components for generation of low-dimensional descriptor space, and the third between one and 15 bins per principal component axis. During GA optimization, each chromosomeencoded combination of descriptors and calculation parameters of the initial population is submitted to principal component analysis of the descriptor space.